Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.

中文翻译：

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在智能设备中使用嵌入式传感器的基于深度学习的私人信息推理

在过去的十年中，智能移动设备和移动应用程序以迅捷的速度推出，将这些设备转变为方便的通用计算平台。来自智能设备的感官数据是滋养移动服务的重要资源，它们被视为无害信息，无需用户许可即可获取。在本文中，我们证明了这些看似无害的信息可能会导致严重的隐私问题。首先，我们演示了通过使用一些深度学习技术，可以基于感官数据识别用户在智能设备屏幕上的点击位置。其次，表明可以收集每种类型的应用程序的点击流配置文件，从而可以准确地推断出用户的应用程序使用习惯。在我们的实验中 收集了102名志愿者的感官数据和移动应用使用情况信息。实验结果表明，利用卷积神经网络，抽头位置推断的预测精度至少可以达到90％。此外，基于推断出的点击位置信息，可以高精度地推断出用户的应用使用习惯和密码。